MiniMax M2

MiniMax M2 is tuned for practical depth at steady latency. It keeps multi-document prompts coherent, decomposes harder problems, and returns structured outputs that downstream systems can parse without brittle post-processing. The model integrates with retrieval to ground claims, calls tools to search or execute code in loop, and streams tokens for responsive apps. It quantizes well for cost control and adapts cleanly through LoRA or lightweight fine-tunes to add domain knowledge and tone. Teams adopt M2 as a default engine for knowledge assistants, support copilots, analytics explainers, and repo-aware code helpers, routing only the toughest prompts to a heavier tier when extra deliberation is required.